Despite extensive and ongoing efforts to improve our ability to detect and treat cancer, it remains a leading cause of death worldwide, and incidence is only expected to continue rising. Recent advancements in highthroughput omic profiling technologies have facilitated a more thorough understanding of tumor physiology, enabling rapid quantification of molecular-level features with sufficient detail and breadth to begin unraveling the underlying mechanisms that drive malignant transformation. However, the full potential of omics techniques cannot be accessed without the use of systems biology approaches that optimize the extraction of biologically meaningful information from these increasingly large and complex datasets. Using an integrative approach to map omics data to biomolecular networks, such as genome-scale metabolic models, protein-protein interaction networks, and transcription factor signaling networks, constitutes a more effective approach, and has been demonstrated to successfully elucidate novel and medically important features from omics datasets. We propose to employ integrative omics analyses to determine how changes in human metabolic and protein secretory processes drive malignant transformation, and how the tumor phenotype shapes these processes in return. Despite extensive efforts to characterize the individual components of the protein secretory pathway and their role in diseases such as cancer, studies treating these components as a connected, interacting system are lacking. The proposed work seeks to develop a more comprehensive and quantitative understanding of the protein secretion pathway and its interactions with the metabolic network in the context of cancer by integrating multiple layers of top-down omics datasets with bottom-up biological networks. Results obtained from these studies will lead to new candidate biomarkers for use in patient stratification and disease diagnosis, reveal novel mechanistic information on cancer and tumor physiology, and provide new tools with which human omics datasets can be processed. Approaches and tools developed for these efforts will have broad application to other diseases and biological systems, and will help drive the shift in healthcare toward personalized medicine.